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In this paper, we discuss an approach to evaluate decisions made during a multi-armed bandit learning experiment. Usually, the results of machine learning algorithms applied on multi-armed bandit scenarios are rated in terms of earned reward and optimal decisions taken. These criteria are valuable for objective comparison in finite experiments. But learning algorithms used in real scenarios, for example...
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